Not every training example contributes to a network's learning in the same way. Training data examples can differ from each other in quality, how representative they are of their class, and uniqueness. A training sample might have distracting backgrounds, corruptions, or a poor foreground/background ratio. For these reasons and more, training samples have been divided into categories of easy/medium/hard [1]}, [2]}.
This has led to the research question: Which training samples should be learned first and then in what order?
